This invention relates generally to forecasting methods, and more specifically to the adaptation of an expert system in order to forecast product demand for a diverse product line.
Forecasting a factory's product demand using conventional forecasting techniques is a difficult and time-consuming task. However, forecasting is considered to be very necessary in the world of manufacturing where customers often order products long after the lead time for procuring raw material has passed. In order to supply products on schedule, forecasting has come to play a major role in factory operations such as initial planning, scheduling, and financial management.
Conventional forecasting techniques have typically been deficient in a number of ways. For example, the forecasting process was difficult to improve because the forecaster did not have the means to easily experiment with and incorporate new algorithms. Also, many forecasting tasks were manually intensive and a large number of products had to be forecasted. This left little time for improving the forecasting process.
To the extent forecasting was automated, it dealt exclusively with historical order information and could not benefit from up-to-the-minute real-world knowledge. In addition, there was no systematic and automated method for storing and updating product information. Such product information was typically stored in the forecaster's head or written down on scratch paper. Without being able to capture and understand this expert knowledge, it has been virtually impossible to improve the forecasting process.
The order history trend analysis was typically done with intuitive reasoning. Without a systematic method of performing trend analysis, there was no consistent way to generate product forecasts and compare predictions.